Recently, artificial intelligence research has driven the development of stretchable and flexible electronic systems. Conductive hydrogels are a class of soft electronic materials that have emerging applications in wearable and implantable biomedical devices. However, current conductive hydrogels possess fundamental limitations in terms of their antibacterial performance and a mechanical mismatch with human tissues, which severely limits their applications in biological interfaces. Here, inspired by animal skin, a conductive hydrogel is fabricated from a supramolecular assembly of polydopamine decorated silver nanoparticles (PDA@Ag NPs), polyaniline, and polyvinyl alcohol, namely PDA@Ag NPs/CPHs. The resultant hydrogel has many desirable features, such as tunable mechanical and electrochemical properties, eye‐catching processability, good self‐healing ability as well as repeatable adhesiveness. Remarkably, PDA@Ag NPs/CPHs exhibit broad antibacterial activity against Gram‐negative and Gram‐positive bacteria. The potential application of this versatile hydrogel is demonstrated by monitoring large‐scale movements of the human body in real time. In addition, PDA@Ag NPs/CPHs have a significant therapeutic effect on diabetic foot wounds by promoting angiogenesis, accelerating collagen deposition, inhibiting bacterial growth, and controlling wound infection. To the best of the authors' knowledge, this is the first time that conductive hydrogels with antibacterial ability are developed for use as epidermal sensors and diabetic foot wound dressing.

A diabetic foot ulcer (DFUs) is a state of prolonged chronic inflammation, which can result in amputation. Different from normal skin wounds, various commercially available dressings have not sufficiently improved the healing of DFUs. In this study, a novel self-healing hydrogel was prepared by in situ crosslinking of N-carboxyethyl chitosan ( N-chitosan) and adipic acid dihydrazide (ADH) with hyaluronic acid-aldehyde (HA-ALD), to provide a moist and inflammatory relief environment to promote stem cell proliferation or secretion of growth factors, thus accelerating wound healing. The results demonstrated that this injectable and self-healing hydrogel has excellent swelling properties, stability, and mechanical properties. This biocompatible hydrogel stimulated secretion of growth factors from bone marrow mesenchymal stem cells (BM-MSCs) and regulated the inflammatory environment by inhibiting the expression of M1 macrophages and promoting the expression of M2 macrophages, resulting in granulation tissue formation, collagen deposition, nucleated cell proliferation, neovascularization, and enhanced diabetic wound healing. This study showed that N-chitosan/HA-ALD hydrogel could be used as a multifunctional injectable wound dressing to regulate chronic inflammation and provide an optimal environment for BM-MSCs to promote diabetic wound healing.

Diabetic foot ulcers (DFUs) are hard-healing chronic wounds and susceptible to bacterial infection. Conventional hydrogel dressings easily lose water at high temperature or freeze at low temperature, making them unsuitable for long-term use or in extreme environments. Herein, a temperature-tolerant (−20 to 60 °C) antibacterial hydrogel dressing is fabricated by the assembly of polyacrylamide, gelatin, and ε-polylysine. Owing to the water/glycerin (Gly) binary solvent system, the resultant hydrogel (G-PAGL) displayed good heat resistance and antifreezing properties. Within the wide temperature range (−20 to 60 °C), all the desirable features of the hydrogel, including superstretchability (>1400%), enduring water retention, adhesiveness, and persistent antibacterial property, are quite stable. Remarkably, the hydrogel wound dressing displayed lasting and broad antibacterial activity against Gram-positive and Gram-negative bacteria. Satisfactorily, the double-network (DN) G-PAGL hydrogel dressing could effectively promote the healing of DFUs by accelerating collagen deposition, promoting angiogenesis, and inhibiting bacterial breed. As far as we know, this is the first time that the extensive temperature-tolerant DN hydrogel with antibacterial ability is developed to use as DFU wound dressing. The G-PAGL hydrogel provides more choices for DFU wound dressings that could be used in extreme environments.

Alternative polyadenylation (APA) has been implicated to play an important role in post-transcriptional regulation by regulating mRNA abundance, stability, localization and translation, which contributes considerably to transcriptome diversity and gene expression regulation. RNA-seq has become a routine approach for transcriptome profiling, generating unprecedented data that could be used to identify and quantify APA site usage. A number of computational approaches for identifying APA sites and/or dynamic APA events from RNA-seq data have emerged in the literature, which provide valuable yet preliminary results that should be refined to yield credible guidelines for the scientific community. In this review, we provided a comprehensive overview of the status of currently available computational approaches. We also conducted objective benchmarking analysis using RNA-seq data sets from different species (human, mouse and Arabidopsis) and simulated data sets to present a systematic evaluation of 11 representative methods. Our benchmarking study showed that the overall performance of all tools investigated is moderate, reflecting that there is still lot of scope to improve the prediction of APA site or dynamic APA events from RNA-seq data. Particularly, prediction results from individual tools differ considerably, and only a limited number of predicted APA sites or genes are common among different tools. Accordingly, we attempted to give some advice on how to assess the reliability of the obtained results. We also proposed practical recommendations on the appropriate method applicable to diverse scenarios and discussed implications and future directions relevant to profiling APA from RNA-seq data.

More and more neural network approaches have achieved considerable improvement upon submodules of speaker diarization system, including speaker change detection and segment-wise speaker embedding extraction. Still, in the clustering stage, traditional algorithms like probabilistic linear discriminant analysis (PLDA) are widely used for scoring the similarity between two speech segments. In this paper, we propose a supervised method to measure the similarity matrix between all segments of an audio recording with sequential bidirectional long shortterm memory networks (Bi-LSTM). Spectral clustering is applied on top of the similarity matrix to further improve the performance. Experimental results show that our system significantly outperforms the state-of-the-art methods and achieves a diarization error rate of 6.63% on the NIST SRE 2000 CALL-HOME database.